tbuckley/cabot-search huggingface dataset:
Identification of a novel class of early exon ALK rearrangements across two pan tumor sequencing databases.
Authors: Matthew Leong, Xiaomo Li Li, Jacob Mercer, Paul A. Fields, Eden Abraham, Manando Nakasaki, Saleh Heneidi, Matthew Gayhart, Gaurav Khullar, Eric Vail
Journal: Journal of Clinical Oncology (2023)

e15105 Background: ALK-rearrangements are a powerful and highly prevalent driver in cancer biology. The advent of potent tyrosine kinase inhibitors has led to the development of numerous laboratory tests and FDA approved therapies for the detection and treatment of ALK-positive cancers. The vast majority of reported ALK rearrangements involve exon 20 with fusions involving exons 17-19 occasionally reported. While less common, ALK-rearrangements occurring earlier in the ALK gene have not been well characterized. This is largely because most clinically available biomarker testing is only optimized for detection of rearrangements involving the common regions. To this end, we aimed to investigate the prevalence of early exon ALK rearrangements (eALKr). Methods: Two pathology databases, including the Tempus deidentified database, were retrospectively queried to characterize eALKr, which were defined as any rearrangement that involved ALK exons 1-16. Demographic information and tumor type were recorded. Identification of eALKr was performed via targeted amplicon-based RNA sequencing, targeted hybrid capture DNA sequencing and whole transcriptome sequencing. Select specimens with early ALK fusions were also evaluated for ALK detection using available FDA-approved methods (immunohistochemistry [IHC] and fluorescent in-situ hybridization [FISH]). Results: Out of the specimens that underwent NGS testing in the two databases, the prevalence of eALKr was 0.05% (73/143,959) across the databases and 10.3% (73/709) for all tumors with ALK rearrangements. Across the 73 rearrangements, there were 58 unique 5’ partner genes of which 46 were only identified once. eALK rearrangements were identified in all exons from exons 1 to 16 except for exon 13, with exon 4 being the most common. Prostatic, breast and ovarian serous carcinoma were the most common tumor types (Table 1). In three cases IHC and FISH were unreliable due to the early rearrangement location (0/3 IHC positive, 1/3 FISH positive). FISH was negative for samples with rearrangements in exon 2 and 4 and showed an atypical (deletion) pattern with a rearrangement involving exon 12. Conclusions: A non-trivial number of eALKr were detected within two database using next generation sequencing (0.05% of total cancer cases; 10.3% of ALK fusion cases). These novel eALK rearrangements were predominantly in tumor types not normally associated with ALK fusions. FDA approved testing modalities are only optimized to detect common ALK rearrangements, which in part may be a reason why eALK-rearrangements are poorly characterized, with limited prevalence and treatment data available. [Table: see text]
Gene expression comparison between primary aggressive luminal breast cancer and paired axillary and sentinel lymph node metastasis.
Authors: Jiyu Li, Xiao Chen, Wenshan Wang, Chi Zhang, Huimin Wang, Yunjie Song, Xiangyu Yin, Xuejiao Ma, Furong Du
Journal: Journal of Clinical Oncology (2023)

e13060 Background: More aggressive and therapy-resistant hormone receptor (HR)-positive breast cancer (hereafter luminal breast cancer, LBC) remains a great challenge. Few studies examine the genomics of axillary lymph node (ALN) metastasis in LBC. The aim of this study was to characterize and compare the mRNA expression patterns of primary breast cancers and paired ALN metastases. Methods: Aggressive pT1-2N2-3M0 LBC patients aged over 18 years who were treated at Shandong Provincial Hospital Affiliated to Shandong First Medical University and had available matched primary and metastatic tumor samples for gene expression analyses were retrospectively recruited. Bulk mRNA expression levels were determined by RNA sequencing (Illumina NovaSeq 6000 system) and calculated using featureCounts. Differential gene expression analysis was performed using DESeq2. All analyses were carried out with differentially expressed genes (DEGs) with an FDR &lt;0.05. Changes of intrinsic subtypes from primary to metastatic disease were assessed. Results: Ten LBC patients with paired tumor samples were included. Compared with the primary tumor, ALN metastatic tumor had 1509 statistically significant DEGs, including 743 upregulated genes and 766 downregulated genes. Notably, there was an upregulation of genes related to immune response-activating signal transduction, T cell receptor signaling pathway, B cell receptor signaling pathway, and cytokine-cytokine receptor interaction in the ALN metastasis. The downregulation of genes were mainly enriched in the PI3K-Akt signaling pathway, ECM-receptor interaction, Wnt signaling pathway, and TGF-beta signaling pathway. Intrinsic subtype distribution in primary tumors was different from those in ALN metastatic tumors, such as Luminal A (30% vs. 50%), Luminal B (10% vs. 20%), HER2-enriched (20% vs. 10%), Basal-like (10% vs. 10%), and Normal-like (30% vs. 10%). These results suggest that LBC is a highly heterogeneous disease. Conclusions: In aggressive pT1-2N2-3M0 LBC, ALN metastases have a distinct gene expression profile and show upregulation of immune-related genes ( CR2, BLK, PAX5, CD19, FCRL1, TNFRSF13B). Genes associated with signaling pathways are involved in the development of breast cancer ( FST, TNN, LAMC3, SFRP2, FGF18), and are downregulated when compared to autologous primary cancer. These pathways are potential targeted pathways in the treatment of breast cancer. LBC cells metastatic to lymph nodes undergo a change to metastasize and survive in the new microenvironment, which may provide insights into the metastatic process. Besides, a switch towards more aggressive intrinsic subtype from primary to advanced disease is also observed.
Comparative effectiveness of palbociclib plus aromatase inhibitor versus fulvestrant alone as initial endocrine therapy for HR+/HER2- advanced breast cancer in Chinese clinical practice: A real-world study.
Authors: Jian Yue, Peng Yuan, Xue Wang, Jie Ju, Song-lin Gao, Zixuan Yang, Yikun Kang, Zixuan Lei, You Shuai, Tong Wei
Journal: Journal of Clinical Oncology (2023)

e13057 Background: In the real world, there are still a large number of HR+/HER2- advanced breast cancer patients receiving initial endocrine therapy with fulvestrant monotherapy in China, especially patients with bone metastases only. It is due to lack of data comparing the effective between Palbociclib combined with AI and fulvestrant monotherapy as initial endocrine therapy. On the other hand, there are economic factors and access issues. According to the National Cancer Center's previous clinical experience, Palbociclib combined with AI is superior to fulvestrant monotherapy in HR+/HER2- advanced breast cancer patients receiving initial endocrine therapy. Therefore, we collected and summarized the previous clinical data in our center to compare the effective of Palbociclib combined with AI and fulvestrant monotherapy for HR+/ HER2-advanced breast cancer patients receiving initial endocrine therapy in China. Methods: This is a retrospective real-world experience aimed at verifying the effective of Palbociclib combined with AI and fulvestrant monotherapy for HR+/ HER2-advanced breast cancer patients receiving initial endocrine therapy in China. The primary aim was rwPFS; secondary aims were overall survival, and objective response rate. Patients received Palbociclib combined with AI or fulvsetrant monotherapy. Results: In total, 346 patients were enrolled, 193 to Palbociclib plus AI and 153 to fulvestrant monotherapy. Median age was 55 years (range 28-88); 191 had visceral involvement (116 in Palbociclib plus AI group and 75 in fulvestrant group). At a median follow up of 38 months (range 4-78). The median PFS was 20.0 months (95% CI 17.3–22.7) in the Palbociclib plus AI group and 12 months (95% CI 9.6–14.3) in the fulvestrant group (hazard ratio 0.593, 95% CI 0.470–0.749, p&lt;0·0001). The 6-month progression-free rate was 83.9% in the Palbociclib plus AI group and 75.8% in fulvestrant group. The 6-month progression-free rate was 83.9% in the Palbociclib plus AI group and 75.8% in fulvestrant group. The 12-month progression-free rate was 65.8% in the Palbociclib plus AI group and 46.4% in fulvestrant monotherapy group. The over survival was not reached in the Palbociclib plus AI group and it was 65 months (95% CI 55.6–75.4) in the fulvestrant group. Grade 3-4 adverse events occurred in Palbociclib plus AI group. The most common grade 3 or 4 adverse events were neutropenia (47.7%) and leucopenia (27.5%) in the Palbociclib plus AI group. Conclusions: The efficacy of palbociclib plus AI is worthy of recognition and the toxicity was acceptable. Compared with fulvestrant monotherapy in HR+/HER2- advanced breast cancer patients receiving initial endocrine therapy, Palbociclib plus AI might have a better survival. Clinical trial information: NCT05000736 .
The CLARIFY digital decision support platform: An artificial intelligence tool for exploring multidimensional cancer data.
Authors: Maria Torrente, Ernestina Menasalvas, Pedro Da Costa Sousa, Mariano Provencio
Journal: Journal of Clinical Oncology (2023)

e13638 Background: Artificial intelligence has emerged as a mean of improving cancer care with the use of computer science. A digital framework that includes heterogeneous pipelines of real-world data can take advantage of AI, genomics and natural language processing to uncover insights that support decision-making in daily clinical practice. The goal of this study is to present an AI-based solution tool for cancer patients’ and identify clinical factors associated with relapse and survival, and to develop a prognostic model that identifies features associated with poor prognosis and stratifies patients by risk. Methods: This is a hospital-based retrospective registry included in CLARIFY (Cancer Long Survivor Artificial Intelligence Follow-up), a European project supported by the EU Horizon 2020 (grant agreement nº 875160), including 2275 patients diagnosed since 2008 at Medical Oncology Department at Hospital Universitario Puerta de Hierro-Majadahonda with non-small cell lung cancer (NSCLC) and 3000 breast cancer patients. The study was approved by the Ethics Committee at HUPHM (No. PI 148/15) and was carried out in accordance with the Helsinki Declaration. CLARIFY Decision Support Platform (DSP) is an AI-based solution tool which centralizes and analyzes real time anonymized clinical data from heterogeneous sources of data. It shows the clinical user information about individual patients or about the whole population and produces real-time descriptive statistics along with survival analysis (Kaplan Meier estimates and Cox regression model). Integrated data include clinical data from electronic health records from more than 5000 NSCLC patients and breast cancer patients, including more than 1,000,000 clinical notes, more than 900.000 clinical reports, data from wearable devices that produced around 1,000,000 variable values per patient, genomic data and data from quality of life questionnaires. Results: Using the DSP we obtained patients’ profiles, survival probabilities, we stratified over 2000 patients in low and high-risk profile and developed a machine-aided tumour-recurrence prediction model. This computational infrastructure was able to extract knowledge from different data sources allowing clinicians to analyse multiple factors that help stratifying patients by risk, in order to implement a personalized follow up care programme, aiming to make a significant impact in the patients’ quality of life. Conclusions: The reconstruction of the population’s risk profile was achieved and proved useful in clinical practice using AI. This DSP has potential application in clinical settings to improve risk stratification, early detection, and personalized surveillance management of cancer patients and may assist clinicians in their daily clinical practice.
Geographic disparities in Medicare telehealth utilization and access to specialized cancer care in the United States.
Authors: Utkarsh Goel, Misha Gautam, Shipra Gandhi, Arya Mariam Roy
Journal: Journal of Clinical Oncology (2023)

e13683 Background: Access to specialized cancer care varies by place of residence, partly due to differences in geographic proximity to designated cancer centers. Telehealth services can help alleviate some of these geographic disparities. In response to the COVID-19 pandemic, Medicare telehealth services were expanded rapidly. In this study, we sought to describe the geographic variations in Medicare telehealth utilization and their relationship with locations of designated cancer centers in the United States. We hypothesized that differences in telehealth utilization might be reflective of barriers to telehealth access for Medicare beneficiaries in these regions. Methods: We used the Medicare Telehealth Trends data file and calculated the proportion of Medicare users who used a telehealth service (% Telehealth Users) across all quarters of 2021. To obtain % Telehealth Users in a state, the number of telehealth users were divided by the total number of telehealth-eligible users. Then, using % Telehealth Users as a metric of telehealth utilization, we ranked all US states by overall % Telehealth Users, and % Telehealth Users in rural and urban locations. Finally, we correlated these ranks with the presence of an NCI (National Cancer Institute) Designated Cancer Center within the states. Results: The % Telehealth Users ranged from 15%-53% overall, 16%-54% for rural locations, and 14%-54% for urban locations, across lowest ranked to highest ranked states. The 5 lowest ranking states and their respective % Telehealth Users overall were- Wyoming (20%), Montana (20%), Iowa (20%), South Dakota (18%), Nebraska (17%), and North Dakota (15%). The lowest ranking states in terms of rural % Telehealth Users were Montana, Kansas, Iowa, Tennessee, Wyoming (19% each), and South Dakota and Nebraska (16% each). The lowest ranking states in terms of urban % Telehealth Users were Montana (20%), Wyoming (20%), Nebraska (19%), South Dakota (16%), and North Dakota (14%). 4 out of 6 states overall, 3 out of 7 in the rural category, and 4 out of 5 states in the urban category that were ranked the lowest in telehealth utilization did not have an in-state NCI designated cancer center. Conclusions: Telehealth services can bridge gaps in access to specialty cancer care for patients that do not live in geographic proximity of cancer centers. However, regions lacking geographic proximity to cancer centers were also regions with poorest telehealth utilization. Advocacy and policy efforts directed towards increasing access to telehealth can ensure equitable and timely access to specialized cancer care in these regions of need.
Association of patient age and insurance status with in-hospital mortality for patients with lung cancer.
Authors: Jayla Hsiung, Kamil Taneja, Karan Patel, Kevin Metz, Steven Chen, William Benedict, Jared Wolfe, Michael J. Diaz, Jules A. Cohen
Journal: Journal of Clinical Oncology (2023)

e13669 Background: In 2022, the National Cancer Institute estimated that more than 240,000 individuals will be diagnosed with lung cancer. Furthermore, over 130,000 patients will die. To help minimize this death rate, it is important to identify death risk factors. Methods: We utilized the 2006-2012 Nationwide Emergency Department Sample to identify risk risk factors for death in lung cancer patients. A multivariate logistic regression was used to identify patient and hospital characteristics that were significantly associated with a higher chance of in-hospital death. The top 20 primary diagnoses of these patients were included in the analysis to take into account the reason to visit the ED. A p-value less than 0.05 was considered significant. Results: During this time period, 1,344,817 lung cancer patients presented to the emergency departments (ED) across the US. The majority of patients at least 70 years old (50.8%), male (51.1%), Medicare beneficiaries (67.5%), of the lowest income quartile (28.1%), living in the South (40.3%), presented to non-trauma hospitals (54.3%), and presented to metropolitan non-teaching hospitals (43.3%). Out of the 1,344,817 lung cancer patients that presented to the ED, 6.1% died in the hospital. The primary diagnoses associated with increased mortality are the following: respiratory failure (odds ratio (OR): 9.05, 95% confidence interval (CI): 8.47-9.67, p &lt; 0.001), septicemia (OR: 8.46, 95% CI: 7.98-8.97,p &lt; 0.001), acute cerebrovascular disease (OR: 3.04, 95% CI: 2.71-3.35,p &lt; 0.001), secondary malignancy (OR: 2.00, 95% CI: 1.88-2.13,p &lt; 0.001), pulmonary heart disease (OR: 1.97, 95% CI: 1.75-2.20,p &lt; 0.001), and pneumonia (OR: 1.90, 95% CI: 1.80-2.01, p &lt; 0.001). Patients at least 70 years old (OR: 3.09, 95% CI: 1.72-5.53, p &lt; 0.001), males (OR: 1.28, 95% CI: 1.24-1.32,p &lt; 0.001), and uninsured patients (OR: 1.29, 95% CI: 1.15-1.46, p &lt; 0.001) are patient groups at higher risk for in-hospital mortality. 5 primary diagnoses associated with decreased risk of death are the following: fluid/electrolyte disorder (OR: 0.81, 95% CI:0.70-0.93), p = 0.003), central nervous system disease (OR: 0.76, 95% CI: 0.640.91, p = 0.002), arrhythmia (OR: 0.74, 0.65-0.83, p &lt; 0.001), anemia (OR:0.68, 95% CI: 0.55-0.83, &lt; 0.001), complication related to previous surgery of medical therapy (OR:0.64, 95% CI:0.51-0.80, &lt; 0.001), and chronic obstructive pulmonary disease (OR: 0.45, 95% CI: 0.39-0.62, p &lt; 0.001). Conclusions: Older, uninsured, and male lung cancer patients are at high risk for in-hospital mortality. Patients presenting to the ED with respiratory failure and septicemia are at the highest risk for mortality.
The efficacy of cationic amphiphilic antihistamines on outcomes of patients with pancreatic ductal adenocarcinoma.
Authors: Cho Han Chiang, Cho Hung Chiang, Xin Ya See, Yuanping Hsia, Yu-Cheng Chang, Shih-Syuan Wang, Yuan-Jen Chen, Cho Hsien Chiang, Cheng-Ming Peng, Her-Shyong Shiah
Journal: Journal of Clinical Oncology (2023)

e16253 Background: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer with a poor prognosis. H1 antihistamines with cationic amphiphilic properties have been shown to possess antitumor effects and were associated with reduced mortality in several cancer types. We aimed to investigate the effects of cationic amphiphilic antihistamines on the outcomes of patients with PDAC. Methods: We conducted a retrospective cohort study at two tertiary centers in Taiwan between 2010 and 2021. We included all adult patients with histologically confirmed PDAC. Cationic amphiphilic antihistamines included desloratadine, cyproheptadine, and ebastine. The rest of the antihistamines were classified as non-cationic amphiphilic. Patients treated with cationic amphiphilic antihistamines were matched in a 1:2 ratio to patients treated with non-cationic amphiphilic antihistamines based on age, sex, and cancer stage. The primary outcome was overall survival (OS) and the secondary outcomes were progression-free survival (PFS) and objective response rates (ORR). Results: Among 407 patients included in the study, 28 cationic amphiphilic antihistamine users were matched to 56 non-cationic amphiphilic antihistamine users. Over a median follow-up of 6.3 [IQR, 2.0-13.8] months, 67 patients died. Compared with non-users, patients who received cationic amphiphilic antihistamines had a longer OS (median 16.4 [IQR, 2.8 – 89.0] vs. 5.8 [IQR, 2.0 – 9.8] months; p &lt; 0.001) and PFS (median 12.2 [IQR, 2.2 – 83.3] vs. 5.2 [IQR, 1.7 – 8.4] months; p = 0.002). In Cox proportional hazard models, the use of cationic amphiphilic antihistamines was associated with an approximately 60% lower risk of all-cause mortality and disease progression. Cationic amphiphilic antihistamine users also had greater ORR than non-users (complete or partial response, 39% vs. 7%, p = 0.004). Conclusions: The use of cationic amphiphilic antihistamines was associated with improved survival among patients with pancreatic cancer. [Table: see text]
Sequential learning for pan-tumor detection of metastatic disease progression.
Authors: Foad H. Green, Matthew J. Rioth, Joshua Loving
Journal: Journal of Clinical Oncology (2023)

e13591 Background: Detection of progression of patients with early stage cancer is a challenge for real world data from electronic health records (EHR). Temporal patterns common to patients’ data can detect patients with metastatic progression. A deep learning approach on longitudinal patient records of cancer lab testing, visit diagnosis codes, and cancer treatments was used to identify metastatic status. Methods: ICD-10-CM diagnosis codes, clinical lab values specific to cancer testing, and antineoplastic treatments were evaluated from longitudinal data of 128,614 patients who did not progress and 5,884 who did develop metastasis, across 47 tumor types in the Syapse Learning Health Network. Patients metastatic at diagnosis were excluded. Data were included beginning at the time of primary cancer diagnosis and ended for all surviving patients at an established administrative cutoff date. In the metastatic group, visits after the progression event were included but censored if they contained diagnosis codes specifically for secondary malignant neoplasms. A binary classification was developed using a multi-headed attention transformer in order to determine metastatic status. Patient history was sequentially aggregated to visit-level embeddings across their longitudinal record. Categorical layers were used for antineoplastics and diagnosis codes. Linear layers were used to embed lab values, as well as for visit intervals from primary cancer date and administrative cutoff date. All embeddings were used as feature inputs for the classifier. Pre-training random sampling of the non-metastatic population was applied to establish equally-weighted labels in a 80:10:10 split for training, validation, and testing sets; to account for the smaller population of patients who progressed to metastatic status. Results: This classification approach achieved 0.86 PR-AUC, 0.79 ROC-AUC, and 0.75 F1. This performance is comparable to published models trained for single tumor cohort prediction. With our censoring constraints, these model results are robust in the absence of routine signals of metastasis in the EHR, such as staging reports and diagnosis coding for distant metastases. Conclusions: This method generalizes, across multiple cancer types, an accurate classification of metastatic progression from patient visit history. This work is immediately useful for real world evidence data analysis complementing patients with metastases already captured in hospital registries. Using sequential deep learning with EHR data classes, this approach may be used to forecast metastatic progression for early intervention.